Speculative Macro Commit for Faster Tool-Using Agents

Speculative Macro Commit reduces agent waiting time by reusing several tool actions prepared in advance. A smaller model executes predicted actions in an isolated copy of the environment. When the main model chooses the same first action, the system can adopt further prepared steps if they match a learned action pattern and pass additional checks. The paper explains how this skips some main-model decisions.

On telecom tasks, it reduced average latency by 10.23% against single-step speculative execution using the same hardware setup. On AppWorld, latency against that same baseline fell 7.64%, but completed tasks dropped from 70 to 68 out of 168. Reusing longer chains therefore saved time with a measurable completion tradeoff in one test.